17 citations · 20 across the 10 of their papers we have counts for
11 papers
Learning to Focus: Causal Attention Distillation via Gradient-Guided Token Pruning
Yiju Guo, Wenkai Yang, Zexu Sun +3
Large language models (LLMs) have demonstrated significant improvements in contextual understanding. However, their ability to attend to truly critical information during long-cont…
Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing
Zexu Sun, Qiyu Han, Minqin Zhu +3
Improving user engagement and platform revenue is crucial for online marketing platforms. Uplift modeling is proposed to solve this problem, which applies different treatments (e.g…
End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling
Zexu Sun, Hao Yang, Dugang Liu +3
In modern online platforms, incentives are essential factors that enhance user engagement and increase platform revenue. Over recent years, uplift modeling has been introduced as a…
OptDist: Learning Optimal Distribution for Customer Lifetime Value Prediction
Yunpeng Weng, Xing Tang, Zhenhao Xu +4
Customer Lifetime Value (CLTV) prediction is a critical task in business applications. Accurately predicting CLTV is challenging in real-world business scenarios, as the distributi…
Towards Robust Recommendation via Decision Boundary-aware Graph Contrastive Learning
Jiakai Tang, Sunhao Dai, Zexu Sun +6
In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. Howeve…
Rankability-enhanced Revenue Uplift Modeling Framework for Online Marketing
Bowei He, Yunpeng Weng, Xing Tang +5
Uplift modeling has been widely employed in online marketing by predicting the response difference between the treatment and control groups, so as to identify the sensitive individ…
Benchmarking for Deep Uplift Modeling in Online Marketing
Dugang Liu, Xing Tang, Yang Qiao +4
Online marketing is critical for many industrial platforms and business applications, aiming to increase user engagement and platform revenue by identifying corresponding delivery-…
Revisiting Counterfactual Regression through the Lens of Gromov-Wasserstein Information Bottleneck
Hao Yang, Zexu Sun, Hongteng Xu +1
As a promising individualized treatment effect (ITE) estimation method, counterfactual regression (CFR) maps individuals' covariates to a latent space and predicts their counterfac…
MTN: Multi-gate Mixture-of-Experts based Multi-valued Treatment Network for Uplift Modeling
Zexu Sun, Xu Chen
Uplift modeling is a technique used to predict the effect of a treatment (e.g., discounts) on an individual's response. Although several methods have been proposed for multi-valued…
Offline Imitation Learning with Variational Counterfactual Reasoning
Bowei He, Zexu Sun, Jinxin Liu +3
In offline imitation learning (IL), an agent aims to learn an optimal expert behavior policy without additional online environment interactions. However, in many real-world scenari…
Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
Zexu Sun, Bowei He, Ming Ma +4
Uplift modeling has shown very promising results in online marketing. However, most existing works are prone to the robustness challenge in some practical applications. In this pap…